Oil and gas industries have used mathematical predictive models to represent production systems, including wells, subsea and/or surface networks and facilities. Models range from “black oil” to compositional models, and from steady state to transient models. When calibrated with available measurement data, such models can be used to estimate fluid properties and dynamic flow conditions throughout a system. For example, such models may be used to estimate the temperature, pressure and flow rates along the fluid journey. This in turn allows operators to understand potential system problems, such as flow restrictions due to solids buildup, and water and/or condensate buildup in gas lines.
Oil and gas reservoirs may be modeled using non-deterministic methods. For example, geostatistical simulation has been used to capture uncertainty via collections of equal probability realizations (specifically, these methods incorporate uncertainty by varying uncertain parameters, generating a collection of models that all satisfy the available measurements, e.g., seismic, geological, well logs, production history, etc.). Other methods, such as ensemble Kalman Filtering, may also be used to represent model uncertainty and to continuously update reservoir models.
Bayesian techniques may be applied to represent uncertainty in subsurface pore pressure related to seismic, acoustic and other data. However, Bayesian techniques have not been used in conventional methods to provide a continuous update of the uncertainty of models for oil and gas production systems.
Conventional methods, systems, and apparatuses for modeling oil and gas reservoirs, wells, networks and facilities are not ideal in all respects. Thus, there is a need for using non-deterministic techniques, such as Bayesian techniques, to represent uncertainties and provide continuous update of models for oil and gas wells, networks and facilities.